The company behind the AI operating system, VAST Data, has revealed that it is working on an extended partnership with AMD which will aid in the deployment and management of AI factories by AI cloud service providers and enterprises. The collaboration includes the combination of the AI Operating System from VAST and 6th Gen AMD EPYC™ server processors along with the AMD Instinct GPUs.
With companies moving from their first phase of modeling to operational real-time reasoning models and advanced AI agents, data centers needs are changing quickly. To get performance at production scale for AI, it’s not just a matter of raw compute anymore – it takes a systems approach that optimizes memory, context, data, and computing power.
Overcoming Inference and RAG Infrastructure Bottlenecks
Traditional data center architectures designed primarily for model training often encounter performance bottlenecks when supporting retrieval-augmented generation (RAG) pipelines and multi-turn agentic inferencing. These limitations frequently lead to underutilized hardware, rising token costs, and complex storage management.
To overcome these obstacles, VAST Data and AMD are delivering a unified hardware-and-software stack built on VAST’s Disaggregated Shared Everything (DASE) architecture. The platform features robust multi-tenancy isolation, native multi-protocol support, and a unified global namespace that streamlines data operations such as rapid model loading to optimize compute hardware utilization across massive, concurrent workloads.
Key Pillars of the VAST and AMD Technical Integration
- 6th Gen AMD EPYC Processors (Codenamed “Venice”): Selected to power VAST’s 6th-generation CBox and 3rd-generation EBox platforms. Featuring PCIe® Gen-6 support, the processors deliver double the generational I/O bandwidth, reducing latency for database, data warehouse, and event streaming tasks via VAST’s DataBase and DataEngine services.
- Turnkey AI Reference Architectures: Developed alongside DriveNets, these reference designs combine AMD Helios rack-scale systems, the VAST AI OS, and DriveNets AI Fabric networking. The blueprints offer detailed sizing and implementation guidance for model training, reinforcement learning (RL), inference, and KV cache workloads.
- Significant Inference Acceleration: Performance testing using AMD Instinct MI355X GPUs combined with AMD Infinity Context, AMD ROCm™ software, and the VAST AI OS demonstrated a 9x improvement in time-to-first-token (TTFT) and a 9.7x increase in token throughput for high-concurrency agentic AI workloads utilizing VAST for KV cache offloading.
- Automated KV Cache Lifecycle Management: Utilizes VAST’s native data governance policies to automatically expire and delete KV cache data containing sensitive information, simplifying enterprise regulatory compliance without manual overhead.
- High-Speed Network Interconnect: Integrates the AMD Pensando™ Pollara 400 AI NIC via NFS over TCP and NFS over RDMA to move data between GPU memory and VAST’s NVMe SSD storage cluster.
- Ecosystem Software Collaboration: Expanded alliances with AI software developers, including TensorMesh and EmbeddedLLM, to accelerate the rollout of production-ready agentic AI frameworks.
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Executive Commentary
“AI is entering an operational phase where infrastructure efficiency matters as much as model performance,” said John Mao, Vice President, Global Technology Alliances at VAST Data. “The industry is discovering that inference is fundamentally a data problem. Success depends on how effectively organizations can bring data, compute, memory and intelligence together as a single system. The VAST AI Operating System was built for this transition, giving AI cloud providers and enterprises a more efficient, scalable and open foundation for training, inference and the next generation of agentic AI applications.”
“The future of AI will be built on an open ecosystem that gives organizations the flexibility to choose the technologies that best meet their performance, operational and business requirements,” said Derek Dicker, Corporate Vice President, Enterprise Business Group at AMD. “Our expanded collaboration with VAST combines AMD EPYC CPUs and Instinct GPUs with the software foundation customers need to accelerate inference, improve infrastructure efficiency and deploy AI at scale. Together, we’re enabling AI clouds and enterprises to build high-performance AI factories without compromise.”
Industry Partner Statements
“As AI moves into production, customers need infrastructure that can securely and reliably support increasingly complex workloads without sacrificing performance,” said Raghu Chakravarthi, EVP of Engineering and General Manager – Americas at Core42. “Our infrastructure strategy is built on a silicon agnostic approach to give customers the best optionality and output for their use cases. The combination of AMD accelerated computing and the VAST AI Operating System provides a powerful foundation for delivering enterprise-grade AI services across sovereign and commercial environments with the scale, efficiency and operational simplicity our customers expect.”
“Every frontier lab, every AI-native company building the future of AI needs infrastructure that scales as fast as their ambitions,” said Erwan Menard, SVP Product Management at Crusoe. “Our collaboration with AMD and VAST gives Crusoe Cloud customers a validated foundation purpose-built for AI — combining accelerated compute performance with the data and storage scalability that training and inference workloads demand, so they can spend less time integrating infrastructure and more time building their future.”
“Organizations want the freedom to build AI on the infrastructure that best fits their needs,” said Kevin Cochrane, CMO at Vultr. “The collaboration between AMD and VAST gives customers more flexibility in how and where they deploy AI, as well as the scale and performance required for enterprise workloads. That’s exactly the kind of flexibility customers are looking for as they scale AI across diverse environments.”
“Many organizations understand the potential of AI but are still working through the operational realities of deploying it at scale,” said David Bitton, Vice President, AI and Product Strategy at 5C. “Validated architectures developed by AMD and VAST help reduce complexity, accelerate deployment and give customers greater confidence as they move from pilots to production AI environments.”
“The AI market is increasingly looking for high-performance alternatives that provide both scalability and flexibility,” said Piotr Tomasik, President & COO and Co-Founder at TensorWave. “As an AMD-centric AI cloud, we’ve seen firsthand how rapidly the ecosystem has evolved. Collaborations like this help accelerate adoption by giving customers a validated architecture that combines modern AI infrastructure software with AMD accelerated computing.”
“Agentic AI moves the inference bottleneck beyond raw compute to context: long-running, multi-turn agents make cache, state, and data movement part of the performance path itself,” said Pin Siang Tan, CTO, Embedded LLM. “Embedded LLM works with AI clouds and enterprises to turn AMD accelerator capacity into production AI services, and we’re collaborating with VAST to make its data platform the persistent foundation of that work, from KV-cache reuse today to agent state, replay, and reinforcement-learning data ahead.”
“One of the biggest challenges in large-scale AI is efficiently managing large and concurrent context windows as agents retrieve, reason over, and generate information across massive datasets,” said Dhanaseker Kandhasamy, Co-Founder at Phanos.AI. “At Phanos.AI, we leverage VAST and AMD capabilities including purpose-built storage, compute, network, and software-stack observability to drive real-time decision-making on workload placement for optimal performance. The innovations AMD and VAST are bringing to inference infrastructure help address a critical bottleneck for organizations deploying agentic AI at scale.”
“Customers increasingly want the flexibility to build AI infrastructure using the technologies that best meet their requirements,” said Yossi Kikozashvili, Vice President, Head of Product & G2M, AI Infra at DriveNets. “The collaboration between DriveNets, AMD and VAST demonstrates how an open ecosystem can bring together best-in-class networking, accelerated computing and intelligent data infrastructure to support the next generation of AI factories.”
